A decision tree for tuberculosis contact investigation

Lynn B. Gerald, Shenghui Tang, Frank Bruce, David Redden, Michael E. Kimerling, Nancy Brook, Nancy Dunlap, William C. Bailey

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


The University of Alabama at Birmingham and the Alabama Department of Public Health recently developed a logistic regression model showing those variables that are most likely to predict a positive tuberculin skin test in contacts of tuberculosis cases. However, translating such a model into field application requires a stepwise approach. This article describes a decision tree developed to assist public health workers in determining which contacts are most likely to have a positive tuberculin skin test. The Classification and Regression Tree analysis was performed on 292 consecutive cases and their 2,941 contacts seen by the Alabama Department of Public Health from January 1, 1998, to October 15, 1998. Several decision trees were developed and were then tested using prospectively collected data from 366 new tuberculosis cases and their 3,162 contacts from October 15, 1998, to April 30, 2000. Testing showed the trees to have sensitivities of 87-94%, specificities of 22-28%, and false-negative rates between 7 and 10%. The use of the decision trees would decrease the number of contacts investigated by 17-25% while maintaining a false-negative rate that was close to that of the presumed background rate of latent tuberculosis infection in the state of Alabama.

Original languageEnglish (US)
Pages (from-to)1122-1127
Number of pages6
JournalAmerican journal of respiratory and critical care medicine
Issue number8
StatePublished - Oct 15 2002


  • Classification and Regression Tree analysis
  • Contact investigation
  • Decision tree
  • Tuberculosis

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine


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